Synthetic COVID-19 booster effectiveness datasets, requirements register and run artefacts for FAIR Data Train data visiting scenarios

Data accompanying the paper Extending the FAIR Data Train architecture through fixed and adaptive data visiting implementations. Version v1.1-paper1 adds the requirements register and the run artefacts to the datasets of v1.0-paper1, which are unchanged. Synthetic datasets (one archive per use case): each RDF N-Triples file is the local dataset of one simulated FAIR Data Station visited in UC1 (fan-out and pool with capability matching), UC2 (a result-dependent sequence followed by fan-out) or UC3 (train-directed sequential visiting; not evaluated in the paper). The hospital cohorts follow a test-negative design for COVID-19 booster effectiveness against hospitalisation, with a known true effectiveness per site; ground-truth.json records the generation parameters and expected results. All records are synthetic; no real person or hospital is described. Requirements register (requirements_register_coverage.xlsx, with CSV exports): 164 architectural requirements for privacy-preserving data visiting consolidated from the literature (10 invariants, 144 layer requirements in L1–L11, 10 quality attributes), each with a stable identifier, its primary sources, and its UC1 and UC2 coverage (covered, partial or not addressed). Run artefacts (fdt-run-artefacts-v1.1-paper1.zip): UC1 and UC2 run from the published code release with the archived container images: the complete console logs, the handler's run and job records, and every artefact the stations returned (aggregate counts and digests only). Both reproduce the paper's figures exactly. The generators in the code record (https://doi.org/10.5281/zenodo.23915575) reproduce every dataset byte for byte. README.md documents the files, the vocabulary and the register.